Embodiments described herein relate generally to the field of computer software. In particular, embodiments described herein relate to information governance crowd sourcing.
The quality of information assets is a core concern of most modern enterprises. In many cases, information quality has become a key aspect of projects such as data warehousing and application system consolidation. In other cases, it is the main driving force for establishing master data management projects, which aim to create and maintain master data (i.e., customer, supplier, product, employee, account data) at its core. Since these master data entities are critical to all major business processes, the projects strive to maintain premium information quality metrics for the entire enterprise life cycle.
Information quality has multiple metrics, which include, but are not limited to: spelling errors, missing data, duplicate data, incorrect values, inconsistent format, incomplete format, syntax violations, violations of integrity constraints, text formatting, synonyms, and homonyms. An error related to any of these metrics requires human intervention for a resolution, yet current methods fail to optimize human resources for completing these tasks.